Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations142193
Missing cells316559
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.9 MiB
Average record size in memory560.0 B

Variable types

DateTime1
Categorical4
Numeric17
Boolean2

Alerts

Cloud3pm is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
Cloud9am is highly overall correlated with Cloud3pm and 2 other fieldsHigh correlation
Evaporation is highly overall correlated with Humidity9am and 4 other fieldsHigh correlation
Humidity3pm is highly overall correlated with Cloud3pm and 5 other fieldsHigh correlation
Humidity9am is highly overall correlated with Evaporation and 2 other fieldsHigh correlation
MaxTemp is highly overall correlated with Evaporation and 3 other fieldsHigh correlation
MinTemp is highly overall correlated with Evaporation and 3 other fieldsHigh correlation
Pressure3pm is highly overall correlated with Pressure9amHigh correlation
Pressure9am is highly overall correlated with Pressure3pmHigh correlation
RISK_MM is highly overall correlated with Humidity3pm and 1 other fieldsHigh correlation
Sunshine is highly overall correlated with Cloud3pm and 5 other fieldsHigh correlation
Temp3pm is highly overall correlated with Evaporation and 5 other fieldsHigh correlation
Temp9am is highly overall correlated with Evaporation and 3 other fieldsHigh correlation
WindGustSpeed is highly overall correlated with WindSpeed3pm and 1 other fieldsHigh correlation
WindSpeed3pm is highly overall correlated with WindGustSpeedHigh correlation
WindSpeed9am is highly overall correlated with WindGustSpeedHigh correlation
Evaporation has 60843 (42.8%) missing values Missing
Sunshine has 67816 (47.7%) missing values Missing
WindGustDir has 9330 (6.6%) missing values Missing
WindGustSpeed has 9270 (6.5%) missing values Missing
WindDir9am has 10013 (7.0%) missing values Missing
WindDir3pm has 3778 (2.7%) missing values Missing
WindSpeed3pm has 2630 (1.8%) missing values Missing
Humidity9am has 1774 (1.2%) missing values Missing
Humidity3pm has 3610 (2.5%) missing values Missing
Pressure9am has 14014 (9.9%) missing values Missing
Pressure3pm has 13981 (9.8%) missing values Missing
Cloud9am has 53657 (37.7%) missing values Missing
Cloud3pm has 57094 (40.2%) missing values Missing
Temp3pm has 2726 (1.9%) missing values Missing
Rainfall has 90275 (63.5%) zeros Zeros
Sunshine has 2308 (1.6%) zeros Zeros
WindSpeed9am has 8612 (6.1%) zeros Zeros
Cloud9am has 8587 (6.0%) zeros Zeros
Cloud3pm has 4957 (3.5%) zeros Zeros
RISK_MM has 91077 (64.1%) zeros Zeros

Reproduction

Analysis started2025-07-05 11:42:22.820034
Analysis finished2025-07-05 11:43:16.339885
Duration53.52 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Date
Date

Distinct3436
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2007-11-01 00:00:00
Maximum2017-06-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-05T11:43:16.426223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:16.541584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Location
Categorical

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Canberra
 
3418
Sydney
 
3337
Perth
 
3193
Darwin
 
3192
Hobart
 
3188
Other values (44)
125865 

Length

Max length16
Median length11
Mean length8.7032062
Min length4

Characters and Unicode

Total characters1237535
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbury
2nd rowAlbury
3rd rowAlbury
4th rowAlbury
5th rowAlbury

Common Values

ValueCountFrequency (%)
Canberra 3418
 
2.4%
Sydney 3337
 
2.3%
Perth 3193
 
2.2%
Darwin 3192
 
2.2%
Hobart 3188
 
2.2%
Brisbane 3161
 
2.2%
Adelaide 3090
 
2.2%
Bendigo 3034
 
2.1%
Townsville 3033
 
2.1%
AliceSprings 3031
 
2.1%
Other values (39) 110516
77.7%

Length

2025-07-05T11:43:16.656409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canberra 3418
 
2.4%
sydney 3337
 
2.3%
perth 3193
 
2.2%
darwin 3192
 
2.2%
hobart 3188
 
2.2%
brisbane 3161
 
2.2%
adelaide 3090
 
2.2%
bendigo 3034
 
2.1%
townsville 3033
 
2.1%
alicesprings 3031
 
2.1%
Other values (39) 110516
77.7%

Most occurring characters

ValueCountFrequency (%)
a 115725
 
9.4%
r 114339
 
9.2%
o 106342
 
8.6%
e 101043
 
8.2%
n 88369
 
7.1%
l 76287
 
6.2%
i 74395
 
6.0%
t 58309
 
4.7%
d 36311
 
2.9%
s 35975
 
2.9%
Other values (30) 430440
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1237535
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 115725
 
9.4%
r 114339
 
9.2%
o 106342
 
8.6%
e 101043
 
8.2%
n 88369
 
7.1%
l 76287
 
6.2%
i 74395
 
6.0%
t 58309
 
4.7%
d 36311
 
2.9%
s 35975
 
2.9%
Other values (30) 430440
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1237535
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 115725
 
9.4%
r 114339
 
9.2%
o 106342
 
8.6%
e 101043
 
8.2%
n 88369
 
7.1%
l 76287
 
6.2%
i 74395
 
6.0%
t 58309
 
4.7%
d 36311
 
2.9%
s 35975
 
2.9%
Other values (30) 430440
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1237535
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 115725
 
9.4%
r 114339
 
9.2%
o 106342
 
8.6%
e 101043
 
8.2%
n 88369
 
7.1%
l 76287
 
6.2%
i 74395
 
6.0%
t 58309
 
4.7%
d 36311
 
2.9%
s 35975
 
2.9%
Other values (30) 430440
34.8%

MinTemp
Real number (ℝ)

High correlation 

Distinct389
Distinct (%)0.3%
Missing637
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean12.1864
Minimum-8.5
Maximum33.9
Zeros156
Zeros (%)0.1%
Negative3406
Negative (%)2.4%
Memory size1.1 MiB
2025-07-05T11:43:16.755336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.6
median12
Q316.8
95-th percentile23
Maximum33.9
Range42.4
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation6.4032827
Coefficient of variation (CV)0.52544499
Kurtosis-0.48725275
Mean12.1864
Median Absolute Deviation (MAD)4.6
Skewness0.023899821
Sum1725058
Variance41.002029
MonotonicityNot monotonic
2025-07-05T11:43:17.362365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6 883
 
0.6%
11 883
 
0.6%
10.2 880
 
0.6%
10.5 867
 
0.6%
10.8 860
 
0.6%
9 853
 
0.6%
12 850
 
0.6%
10 849
 
0.6%
13 844
 
0.6%
8.9 842
 
0.6%
Other values (379) 132945
93.5%
ValueCountFrequency (%)
-8.5 1
 
< 0.1%
-8.2 2
 
< 0.1%
-8 2
 
< 0.1%
-7.8 1
 
< 0.1%
-7.6 2
 
< 0.1%
-7.5 2
 
< 0.1%
-7.3 1
 
< 0.1%
-7.2 1
 
< 0.1%
-7.1 1
 
< 0.1%
-7 6
< 0.1%
ValueCountFrequency (%)
33.9 1
 
< 0.1%
31.9 1
 
< 0.1%
31.8 1
 
< 0.1%
31.4 3
< 0.1%
31.2 1
 
< 0.1%
31 1
 
< 0.1%
30.7 2
< 0.1%
30.5 1
 
< 0.1%
30.3 1
 
< 0.1%
30.2 1
 
< 0.1%

MaxTemp
Real number (ℝ)

High correlation 

Distinct505
Distinct (%)0.4%
Missing322
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean23.226784
Minimum-4.8
Maximum48.1
Zeros14
Zeros (%)< 0.1%
Negative105
Negative (%)0.1%
Memory size1.1 MiB
2025-07-05T11:43:17.477139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.8
Q117.9
median22.6
Q328.2
95-th percentile35.5
Maximum48.1
Range52.9
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation7.1176181
Coefficient of variation (CV)0.3064401
Kurtosis-0.23844615
Mean23.226784
Median Absolute Deviation (MAD)5.1
Skewness0.22491661
Sum3295207.1
Variance50.660488
MonotonicityNot monotonic
2025-07-05T11:43:17.589461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 871
 
0.6%
19.8 829
 
0.6%
19 827
 
0.6%
20.4 820
 
0.6%
20.8 804
 
0.6%
19.9 803
 
0.6%
19.5 801
 
0.6%
21 799
 
0.6%
18.5 793
 
0.6%
18.2 792
 
0.6%
Other values (495) 133732
94.0%
ValueCountFrequency (%)
-4.8 1
< 0.1%
-4.1 1
< 0.1%
-3.8 1
< 0.1%
-3.7 1
< 0.1%
-3.2 1
< 0.1%
-3.1 2
< 0.1%
-3 1
< 0.1%
-2.9 1
< 0.1%
-2.7 1
< 0.1%
-2.5 2
< 0.1%
ValueCountFrequency (%)
48.1 1
 
< 0.1%
47.3 2
< 0.1%
47 1
 
< 0.1%
46.9 1
 
< 0.1%
46.8 3
< 0.1%
46.7 2
< 0.1%
46.6 1
 
< 0.1%
46.5 1
 
< 0.1%
46.4 4
< 0.1%
46.3 2
< 0.1%

Rainfall
Real number (ℝ)

Zeros 

Distinct679
Distinct (%)0.5%
Missing1406
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.3499741
Minimum0
Maximum371
Zeros90275
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:17.697922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.4651729
Coefficient of variation (CV)3.602241
Kurtosis180.0021
Mean2.3499741
Median Absolute Deviation (MAD)0
Skewness9.8880611
Sum330845.8
Variance71.659153
MonotonicityNot monotonic
2025-07-05T11:43:17.804264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90275
63.5%
0.2 8685
 
6.1%
0.4 3750
 
2.6%
0.6 2562
 
1.8%
0.8 2028
 
1.4%
1 1747
 
1.2%
1.2 1515
 
1.1%
1.4 1365
 
1.0%
1.6 1187
 
0.8%
1.8 1088
 
0.8%
Other values (669) 26585
 
18.7%
(Missing) 1406
 
1.0%
ValueCountFrequency (%)
0 90275
63.5%
0.1 154
 
0.1%
0.2 8685
 
6.1%
0.3 64
 
< 0.1%
0.4 3750
 
2.6%
0.5 39
 
< 0.1%
0.6 2562
 
1.8%
0.7 13
 
< 0.1%
0.8 2028
 
1.4%
0.9 15
 
< 0.1%
ValueCountFrequency (%)
371 1
< 0.1%
367.6 1
< 0.1%
278.4 1
< 0.1%
268.6 1
< 0.1%
247.2 1
< 0.1%
240 1
< 0.1%
236.8 1
< 0.1%
225 1
< 0.1%
219.6 1
< 0.1%
216.3 1
< 0.1%

Evaporation
Real number (ℝ)

High correlation  Missing 

Distinct356
Distinct (%)0.4%
Missing60843
Missing (%)42.8%
Infinite0
Infinite (%)0.0%
Mean5.4698242
Minimum0
Maximum145
Zeros240
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:17.927107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median4.8
Q37.4
95-th percentile12
Maximum145
Range145
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.1885365
Coefficient of variation (CV)0.7657534
Kurtosis45.067784
Mean5.4698242
Median Absolute Deviation (MAD)2.4
Skewness3.746834
Sum444970.2
Variance17.543838
MonotonicityNot monotonic
2025-07-05T11:43:18.042811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 3282
 
2.3%
8 2574
 
1.8%
2.2 2057
 
1.4%
2 1996
 
1.4%
2.6 1975
 
1.4%
2.4 1963
 
1.4%
1.8 1945
 
1.4%
3 1937
 
1.4%
3.4 1934
 
1.4%
3.2 1918
 
1.3%
Other values (346) 59769
42.0%
(Missing) 60843
42.8%
ValueCountFrequency (%)
0 240
 
0.2%
0.1 8
 
< 0.1%
0.2 497
 
0.3%
0.3 10
 
< 0.1%
0.4 760
0.5%
0.5 14
 
< 0.1%
0.6 1082
0.8%
0.7 24
 
< 0.1%
0.8 1358
1.0%
0.9 28
 
< 0.1%
ValueCountFrequency (%)
145 1
< 0.1%
86.2 1
< 0.1%
82.4 1
< 0.1%
81.2 1
< 0.1%
77.3 1
< 0.1%
74.8 1
< 0.1%
72.2 1
< 0.1%
70.4 1
< 0.1%
70 1
< 0.1%
68.8 2
< 0.1%

Sunshine
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct145
Distinct (%)0.2%
Missing67816
Missing (%)47.7%
Infinite0
Infinite (%)0.0%
Mean7.6248531
Minimum0
Maximum14.5
Zeros2308
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:18.154460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14.9
median8.5
Q310.6
95-th percentile12.8
Maximum14.5
Range14.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation3.781525
Coefficient of variation (CV)0.49594726
Kurtosis-0.8203637
Mean7.6248531
Median Absolute Deviation (MAD)2.6
Skewness-0.50291128
Sum567113.7
Variance14.299931
MonotonicityNot monotonic
2025-07-05T11:43:18.278442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2308
 
1.6%
10.7 1087
 
0.8%
11 1078
 
0.8%
10.8 1058
 
0.7%
10.5 1018
 
0.7%
10.9 1013
 
0.7%
10.3 999
 
0.7%
10.2 985
 
0.7%
10 973
 
0.7%
11.1 967
 
0.7%
Other values (135) 62891
44.2%
(Missing) 67816
47.7%
ValueCountFrequency (%)
0 2308
1.6%
0.1 533
 
0.4%
0.2 511
 
0.4%
0.3 422
 
0.3%
0.4 319
 
0.2%
0.5 315
 
0.2%
0.6 293
 
0.2%
0.7 338
 
0.2%
0.8 314
 
0.2%
0.9 318
 
0.2%
ValueCountFrequency (%)
14.5 1
 
< 0.1%
14.3 4
 
< 0.1%
14.2 2
 
< 0.1%
14.1 6
 
< 0.1%
14 15
 
< 0.1%
13.9 22
 
< 0.1%
13.8 56
 
< 0.1%
13.7 117
0.1%
13.6 180
0.1%
13.5 181
0.1%

WindGustDir
Categorical

Missing 

Distinct16
Distinct (%)< 0.1%
Missing9330
Missing (%)6.6%
Memory size8.1 MiB
W
9780 
SE
9309 
E
9071 
N
9033 
SSE
8993 
Other values (11)
86677 

Length

Max length3
Median length2
Mean length2.195901
Min length1

Characters and Unicode

Total characters291754
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowWNW
3rd rowWSW
4th rowNE
5th rowW

Common Values

ValueCountFrequency (%)
W 9780
 
6.9%
SE 9309
 
6.5%
E 9071
 
6.4%
N 9033
 
6.4%
SSE 8993
 
6.3%
S 8949
 
6.3%
WSW 8901
 
6.3%
SW 8797
 
6.2%
SSW 8610
 
6.1%
WNW 8066
 
5.7%
Other values (6) 43354
30.5%
(Missing) 9330
 
6.6%

Length

2025-07-05T11:43:18.389739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w 9780
 
7.4%
se 9309
 
7.0%
e 9071
 
6.8%
n 9033
 
6.8%
sse 8993
 
6.8%
s 8949
 
6.7%
wsw 8901
 
6.7%
sw 8797
 
6.6%
ssw 8610
 
6.5%
wnw 8066
 
6.1%
Other values (6) 43354
32.6%

Most occurring characters

ValueCountFrequency (%)
S 78467
26.9%
W 75685
25.9%
E 71460
24.5%
N 66142
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 78467
26.9%
W 75685
25.9%
E 71460
24.5%
N 66142
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 78467
26.9%
W 75685
25.9%
E 71460
24.5%
N 66142
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 78467
26.9%
W 75685
25.9%
E 71460
24.5%
N 66142
22.7%

WindGustSpeed
Real number (ℝ)

High correlation  Missing 

Distinct67
Distinct (%)0.1%
Missing9270
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean39.984292
Minimum6
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:18.496491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q131
median39
Q348
95-th percentile65
Maximum135
Range129
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.588801
Coefficient of variation (CV)0.33985348
Kurtosis1.4178546
Mean39.984292
Median Absolute Deviation (MAD)9
Skewness0.87430457
Sum5314832
Variance184.65551
MonotonicityNot monotonic
2025-07-05T11:43:18.614610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 9070
 
6.4%
39 8656
 
6.1%
31 8310
 
5.8%
37 7903
 
5.6%
33 7814
 
5.5%
41 7236
 
5.1%
30 6943
 
4.9%
43 6513
 
4.6%
28 6382
 
4.5%
44 5341
 
3.8%
Other values (57) 58755
41.3%
(Missing) 9270
 
6.5%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 18
 
< 0.1%
9 91
 
0.1%
11 190
 
0.1%
13 529
 
0.4%
15 829
 
0.6%
17 1375
1.0%
19 1728
1.2%
20 2598
1.8%
22 2787
2.0%
ValueCountFrequency (%)
135 3
 
< 0.1%
130 1
 
< 0.1%
126 2
 
< 0.1%
124 2
 
< 0.1%
122 2
 
< 0.1%
120 3
 
< 0.1%
117 4
< 0.1%
115 5
< 0.1%
113 8
< 0.1%
111 3
 
< 0.1%

WindDir9am
Categorical

Missing 

Distinct16
Distinct (%)< 0.1%
Missing10013
Missing (%)7.0%
Memory size8.1 MiB
N
11393 
SE
9162 
E
9024 
SSE
8966 
NW
8552 
Other values (11)
85083 

Length

Max length3
Median length2
Mean length2.1843093
Min length1

Characters and Unicode

Total characters288722
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowNNW
3rd rowW
4th rowSE
5th rowENE

Common Values

ValueCountFrequency (%)
N 11393
 
8.0%
SE 9162
 
6.4%
E 9024
 
6.3%
SSE 8966
 
6.3%
NW 8552
 
6.0%
S 8493
 
6.0%
W 8260
 
5.8%
SW 8237
 
5.8%
NNE 7948
 
5.6%
NNW 7840
 
5.5%
Other values (6) 44305
31.2%
(Missing) 10013
 
7.0%

Length

2025-07-05T11:43:18.730144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 11393
 
8.6%
se 9162
 
6.9%
e 9024
 
6.8%
sse 8966
 
6.8%
nw 8552
 
6.5%
s 8493
 
6.4%
w 8260
 
6.2%
sw 8237
 
6.2%
nne 7948
 
6.0%
nnw 7840
 
5.9%
Other values (6) 44305
33.5%

Most occurring characters

ValueCountFrequency (%)
N 73977
25.6%
E 73213
25.4%
S 73121
25.3%
W 68411
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 288722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 73977
25.6%
E 73213
25.4%
S 73121
25.3%
W 68411
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 288722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 73977
25.6%
E 73213
25.4%
S 73121
25.3%
W 68411
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 288722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 73977
25.6%
E 73213
25.4%
S 73121
25.3%
W 68411
23.7%

WindDir3pm
Categorical

Missing 

Distinct16
Distinct (%)< 0.1%
Missing3778
Missing (%)2.7%
Memory size8.0 MiB
SE
10663 
W
9911 
S
9598 
WSW
9329 
SW
9182 
Other values (11)
89732 

Length

Max length3
Median length2
Mean length2.2088068
Min length1

Characters and Unicode

Total characters305732
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowWSW
3rd rowWSW
4th rowE
5th rowNW

Common Values

ValueCountFrequency (%)
SE 10663
 
7.5%
W 9911
 
7.0%
S 9598
 
6.7%
WSW 9329
 
6.6%
SW 9182
 
6.5%
SSE 9142
 
6.4%
N 8667
 
6.1%
WNW 8656
 
6.1%
NW 8468
 
6.0%
ESE 8382
 
5.9%
Other values (6) 46417
32.6%

Length

2025-07-05T11:43:18.829882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 10663
 
7.7%
w 9911
 
7.2%
s 9598
 
6.9%
wsw 9329
 
6.7%
sw 9182
 
6.6%
sse 9142
 
6.6%
n 8667
 
6.3%
wnw 8656
 
6.3%
nw 8468
 
6.1%
ese 8382
 
6.1%
Other values (6) 46417
33.5%

Most occurring characters

ValueCountFrequency (%)
S 81458
26.6%
W 79274
25.9%
E 74967
24.5%
N 70033
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 305732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 81458
26.6%
W 79274
25.9%
E 74967
24.5%
N 70033
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 305732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 81458
26.6%
W 79274
25.9%
E 74967
24.5%
N 70033
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 305732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 81458
26.6%
W 79274
25.9%
E 74967
24.5%
N 70033
22.9%

WindSpeed9am
Real number (ℝ)

High correlation  Zeros 

Distinct43
Distinct (%)< 0.1%
Missing1348
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean14.001988
Minimum0
Maximum130
Zeros8612
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:18.945159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum130
Range130
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.8933371
Coefficient of variation (CV)0.63514817
Kurtosis1.2265551
Mean14.001988
Median Absolute Deviation (MAD)6
Skewness0.77549369
Sum1972110
Variance79.091445
MonotonicityNot monotonic
2025-07-05T11:43:19.054164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
9 13400
 
9.4%
13 12851
 
9.0%
11 11514
 
8.1%
17 10599
 
7.5%
7 10587
 
7.4%
15 10396
 
7.3%
6 8989
 
6.3%
0 8612
 
6.1%
19 8579
 
6.0%
20 7904
 
5.6%
Other values (33) 37414
26.3%
ValueCountFrequency (%)
0 8612
6.1%
2 4544
 
3.2%
4 6292
4.4%
6 8989
6.3%
7 10587
7.4%
9 13400
9.4%
11 11514
8.1%
13 12851
9.0%
15 10396
7.3%
17 10599
7.5%
ValueCountFrequency (%)
130 1
 
< 0.1%
87 2
 
< 0.1%
83 1
 
< 0.1%
74 4
 
< 0.1%
72 1
 
< 0.1%
69 2
 
< 0.1%
67 3
 
< 0.1%
65 8
< 0.1%
63 8
< 0.1%
61 11
< 0.1%

WindSpeed3pm
Real number (ℝ)

High correlation  Missing 

Distinct44
Distinct (%)< 0.1%
Missing2630
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean18.637576
Minimum0
Maximum87
Zeros1096
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:19.164483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile34.8
Maximum87
Range87
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.803345
Coefficient of variation (CV)0.47234389
Kurtosis0.77586454
Mean18.637576
Median Absolute Deviation (MAD)6
Skewness0.6314326
Sum2601116
Variance77.498884
MonotonicityNot monotonic
2025-07-05T11:43:19.271159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
13 12338
 
8.7%
17 12306
 
8.7%
20 11504
 
8.1%
15 11301
 
7.9%
19 11034
 
7.8%
11 9844
 
6.9%
9 9577
 
6.7%
24 8846
 
6.2%
22 8410
 
5.9%
28 6395
 
4.5%
Other values (34) 38008
26.7%
ValueCountFrequency (%)
0 1096
 
0.8%
2 1012
 
0.7%
4 2213
 
1.6%
6 3744
 
2.6%
7 5813
4.1%
9 9577
6.7%
11 9844
6.9%
13 12338
8.7%
15 11301
7.9%
17 12306
8.7%
ValueCountFrequency (%)
87 1
 
< 0.1%
83 2
 
< 0.1%
78 1
 
< 0.1%
76 2
 
< 0.1%
74 1
 
< 0.1%
72 2
 
< 0.1%
69 3
 
< 0.1%
67 1
 
< 0.1%
65 17
< 0.1%
63 13
< 0.1%

Humidity9am
Real number (ℝ)

High correlation  Missing 

Distinct101
Distinct (%)0.1%
Missing1774
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean68.84381
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:19.382169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.051293
Coefficient of variation (CV)0.2767321
Kurtosis-0.039245721
Mean68.84381
Median Absolute Deviation (MAD)13
Skewness-0.48282077
Sum9666979
Variance362.95175
MonotonicityNot monotonic
2025-07-05T11:43:19.490773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 3350
 
2.4%
70 2985
 
2.1%
69 2962
 
2.1%
68 2961
 
2.1%
65 2952
 
2.1%
71 2939
 
2.1%
66 2916
 
2.1%
67 2895
 
2.0%
64 2867
 
2.0%
72 2859
 
2.0%
Other values (91) 110733
77.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 5
 
< 0.1%
2 8
 
< 0.1%
3 10
 
< 0.1%
4 20
 
< 0.1%
5 27
 
< 0.1%
6 37
< 0.1%
7 43
< 0.1%
8 56
< 0.1%
9 71
< 0.1%
ValueCountFrequency (%)
100 2827
2.0%
99 3350
2.4%
98 2063
1.5%
97 1757
1.2%
96 1577
1.1%
95 1589
1.1%
94 1730
1.2%
93 1833
1.3%
92 1728
1.2%
91 1834
1.3%

Humidity3pm
Real number (ℝ)

High correlation  Missing 

Distinct101
Distinct (%)0.1%
Missing3610
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean51.482606
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:19.601836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median52
Q366
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.797772
Coefficient of variation (CV)0.40397667
Kurtosis-0.51110119
Mean51.482606
Median Absolute Deviation (MAD)14
Skewness0.034515443
Sum7134614
Variance432.54731
MonotonicityNot monotonic
2025-07-05T11:43:19.717535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 2699
 
1.9%
55 2685
 
1.9%
57 2679
 
1.9%
53 2650
 
1.9%
59 2639
 
1.9%
58 2604
 
1.8%
54 2595
 
1.8%
51 2577
 
1.8%
56 2574
 
1.8%
50 2573
 
1.8%
Other values (91) 112308
79.0%
(Missing) 3610
 
2.5%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 26
 
< 0.1%
2 35
 
< 0.1%
3 63
 
< 0.1%
4 113
 
0.1%
5 156
 
0.1%
6 239
0.2%
7 302
0.2%
8 420
0.3%
9 478
0.3%
ValueCountFrequency (%)
100 393
0.3%
99 428
0.3%
98 588
0.4%
97 393
0.3%
96 451
0.3%
95 452
0.3%
94 550
0.4%
93 593
0.4%
92 636
0.4%
91 601
0.4%

Pressure9am
Real number (ℝ)

High correlation  Missing 

Distinct546
Distinct (%)0.4%
Missing14014
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean1017.6538
Minimum980.5
Maximum1041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:19.826019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.2
Q11012.9
median1017.6
Q31022.4
95-th percentile1029.5
Maximum1041
Range60.5
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.1054757
Coefficient of variation (CV)0.0069822134
Kurtosis0.23619998
Mean1017.6538
Median Absolute Deviation (MAD)4.7
Skewness-0.096210894
Sum1.3044184 × 108
Variance50.487785
MonotonicityNot monotonic
2025-07-05T11:43:19.938739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016.4 804
 
0.6%
1017.9 779
 
0.5%
1018.7 764
 
0.5%
1018 761
 
0.5%
1015.9 757
 
0.5%
1017.3 756
 
0.5%
1017.8 755
 
0.5%
1016.3 753
 
0.5%
1017.2 745
 
0.5%
1015.5 744
 
0.5%
Other values (536) 120561
84.8%
(Missing) 14014
 
9.9%
ValueCountFrequency (%)
980.5 1
< 0.1%
982 1
< 0.1%
982.2 1
< 0.1%
982.3 1
< 0.1%
982.9 2
< 0.1%
983.7 1
< 0.1%
983.9 1
< 0.1%
984.4 1
< 0.1%
984.6 2
< 0.1%
985 1
< 0.1%
ValueCountFrequency (%)
1041 1
 
< 0.1%
1040.9 1
 
< 0.1%
1040.6 2
< 0.1%
1040.5 1
 
< 0.1%
1040.4 3
< 0.1%
1040.3 3
< 0.1%
1040.2 2
< 0.1%
1040.1 3
< 0.1%
1040 1
 
< 0.1%
1039.9 3
< 0.1%

Pressure3pm
Real number (ℝ)

High correlation  Missing 

Distinct549
Distinct (%)0.4%
Missing13981
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean1015.2582
Minimum977.1
Maximum1039.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:20.062611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004
Q11010.4
median1015.2
Q31020
95-th percentile1026.9
Maximum1039.6
Range62.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.0366768
Coefficient of variation (CV)0.0069309233
Kurtosis0.13252079
Mean1015.2582
Median Absolute Deviation (MAD)4.8
Skewness-0.046197619
Sum1.3016828 × 108
Variance49.51482
MonotonicityNot monotonic
2025-07-05T11:43:20.178196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.5 773
 
0.5%
1015.3 767
 
0.5%
1015.7 763
 
0.5%
1015.6 761
 
0.5%
1015.1 752
 
0.5%
1013.5 751
 
0.5%
1015.8 751
 
0.5%
1015.4 745
 
0.5%
1016 738
 
0.5%
1014.8 735
 
0.5%
Other values (539) 120676
84.9%
(Missing) 13981
 
9.8%
ValueCountFrequency (%)
977.1 1
< 0.1%
978.2 1
< 0.1%
979 1
< 0.1%
980.2 2
< 0.1%
981.2 1
< 0.1%
981.4 1
< 0.1%
981.9 1
< 0.1%
982.2 1
< 0.1%
982.6 1
< 0.1%
982.9 1
< 0.1%
ValueCountFrequency (%)
1039.6 1
 
< 0.1%
1038.9 1
 
< 0.1%
1038.5 1
 
< 0.1%
1038.4 1
 
< 0.1%
1038.2 1
 
< 0.1%
1038 1
 
< 0.1%
1037.9 2
< 0.1%
1037.8 2
< 0.1%
1037.7 3
< 0.1%
1037.6 1
 
< 0.1%

Cloud9am
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing53657
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean4.4371894
Minimum0
Maximum9
Zeros8587
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:20.263184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.8870155
Coefficient of variation (CV)0.65064059
Kurtosis-1.5411594
Mean4.4371894
Median Absolute Deviation (MAD)3
Skewness-0.22428554
Sum392851
Variance8.3348586
MonotonicityNot monotonic
2025-07-05T11:43:20.324345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 19749
 
13.9%
1 15558
 
10.9%
8 14389
 
10.1%
0 8587
 
6.0%
6 8072
 
5.7%
2 6442
 
4.5%
3 5854
 
4.1%
5 5510
 
3.9%
4 4373
 
3.1%
9 2
 
< 0.1%
(Missing) 53657
37.7%
ValueCountFrequency (%)
0 8587
6.0%
1 15558
10.9%
2 6442
 
4.5%
3 5854
 
4.1%
4 4373
 
3.1%
5 5510
 
3.9%
6 8072
5.7%
7 19749
13.9%
8 14389
10.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 14389
10.1%
7 19749
13.9%
6 8072
5.7%
5 5510
 
3.9%
4 4373
 
3.1%
3 5854
 
4.1%
2 6442
 
4.5%
1 15558
10.9%
0 8587
6.0%

Cloud3pm
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing57094
Missing (%)40.2%
Infinite0
Infinite (%)0.0%
Mean4.5031669
Minimum0
Maximum9
Zeros4957
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:20.390203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7206325
Coefficient of variation (CV)0.60415983
Kurtosis-1.4579326
Mean4.5031669
Median Absolute Deviation (MAD)2
Skewness-0.22409236
Sum383215
Variance7.4018414
MonotonicityNot monotonic
2025-07-05T11:43:20.457978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 18052
 
12.7%
1 14827
 
10.4%
8 12407
 
8.7%
6 8869
 
6.2%
2 7153
 
5.0%
3 6836
 
4.8%
5 6743
 
4.7%
4 5254
 
3.7%
0 4957
 
3.5%
9 1
 
< 0.1%
(Missing) 57094
40.2%
ValueCountFrequency (%)
0 4957
 
3.5%
1 14827
10.4%
2 7153
 
5.0%
3 6836
 
4.8%
4 5254
 
3.7%
5 6743
 
4.7%
6 8869
6.2%
7 18052
12.7%
8 12407
8.7%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 12407
8.7%
7 18052
12.7%
6 8869
6.2%
5 6743
 
4.7%
4 5254
 
3.7%
3 6836
 
4.8%
2 7153
 
5.0%
1 14827
10.4%
0 4957
 
3.5%

Temp9am
Real number (ℝ)

High correlation 

Distinct440
Distinct (%)0.3%
Missing904
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean16.987509
Minimum-7.2
Maximum40.2
Zeros35
Zeros (%)< 0.1%
Negative420
Negative (%)0.3%
Memory size1.1 MiB
2025-07-05T11:43:20.548947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.3
median16.7
Q321.6
95-th percentile28.2
Maximum40.2
Range47.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.4928383
Coefficient of variation (CV)0.38221251
Kurtosis-0.34915477
Mean16.987509
Median Absolute Deviation (MAD)4.6
Skewness0.09138682
Sum2400148.1
Variance42.15695
MonotonicityNot monotonic
2025-07-05T11:43:20.656738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 901
 
0.6%
13.8 887
 
0.6%
14.8 873
 
0.6%
16 869
 
0.6%
14 855
 
0.6%
16.6 855
 
0.6%
15 852
 
0.6%
16.5 844
 
0.6%
13 831
 
0.6%
14.4 827
 
0.6%
Other values (430) 132695
93.3%
(Missing) 904
 
0.6%
ValueCountFrequency (%)
-7.2 1
 
< 0.1%
-7 1
 
< 0.1%
-6.2 1
 
< 0.1%
-5.9 1
 
< 0.1%
-5.6 2
 
< 0.1%
-5.5 2
 
< 0.1%
-5.3 2
 
< 0.1%
-5.2 5
< 0.1%
-4.8 1
 
< 0.1%
-4.5 2
 
< 0.1%
ValueCountFrequency (%)
40.2 1
< 0.1%
39.4 1
< 0.1%
39.1 1
< 0.1%
39 1
< 0.1%
38.9 1
< 0.1%
38.6 1
< 0.1%
38.3 1
< 0.1%
38.2 1
< 0.1%
38 1
< 0.1%
37.9 1
< 0.1%

Temp3pm
Real number (ℝ)

High correlation  Missing 

Distinct500
Distinct (%)0.4%
Missing2726
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean21.687235
Minimum-5.4
Maximum46.7
Zeros16
Zeros (%)< 0.1%
Negative171
Negative (%)0.1%
Memory size1.1 MiB
2025-07-05T11:43:20.760829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.5
Q116.6
median21.1
Q326.4
95-th percentile33.7
Maximum46.7
Range52.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.9375939
Coefficient of variation (CV)0.31989296
Kurtosis-0.1464607
Mean21.687235
Median Absolute Deviation (MAD)4.9
Skewness0.24005419
Sum3024653.6
Variance48.130209
MonotonicityNot monotonic
2025-07-05T11:43:20.862445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 871
 
0.6%
19 858
 
0.6%
18.4 856
 
0.6%
18.5 856
 
0.6%
17.8 845
 
0.6%
19.2 826
 
0.6%
19.4 825
 
0.6%
19.3 821
 
0.6%
18 821
 
0.6%
17 814
 
0.6%
Other values (490) 131074
92.2%
(Missing) 2726
 
1.9%
ValueCountFrequency (%)
-5.4 1
 
< 0.1%
-5.1 1
 
< 0.1%
-4.4 1
 
< 0.1%
-4.2 1
 
< 0.1%
-4.1 1
 
< 0.1%
-4 1
 
< 0.1%
-3.9 2
< 0.1%
-3.8 1
 
< 0.1%
-3.7 3
< 0.1%
-3.5 3
< 0.1%
ValueCountFrequency (%)
46.7 1
 
< 0.1%
46.2 1
 
< 0.1%
46.1 3
< 0.1%
45.9 1
 
< 0.1%
45.8 2
< 0.1%
45.4 1
 
< 0.1%
45.3 2
< 0.1%
45.2 2
< 0.1%
45 1
 
< 0.1%
44.9 1
 
< 0.1%

RainToday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing1406
Missing (%)1.0%
Memory size277.8 KiB
False
109332 
True
31455 
(Missing)
 
1406
ValueCountFrequency (%)
False 109332
76.9%
True 31455
 
22.1%
(Missing) 1406
 
1.0%
2025-07-05T11:43:20.933747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

RISK_MM
Real number (ℝ)

High correlation  Zeros 

Distinct681
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3606816
Minimum0
Maximum371
Zeros91077
Zeros (%)64.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-05T11:43:21.022242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.4779691
Coefficient of variation (CV)3.5913225
Kurtosis178.16825
Mean2.3606816
Median Absolute Deviation (MAD)0
Skewness9.8369025
Sum335672.4
Variance71.87596
MonotonicityNot monotonic
2025-07-05T11:43:21.139888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91077
64.1%
0.2 8762
 
6.2%
0.4 3781
 
2.7%
0.6 2591
 
1.8%
0.8 2055
 
1.4%
1 1761
 
1.2%
1.2 1535
 
1.1%
1.4 1379
 
1.0%
1.6 1201
 
0.8%
1.8 1104
 
0.8%
Other values (671) 26947
 
19.0%
ValueCountFrequency (%)
0 91077
64.1%
0.1 157
 
0.1%
0.2 8762
 
6.2%
0.3 65
 
< 0.1%
0.4 3781
 
2.7%
0.5 39
 
< 0.1%
0.6 2591
 
1.8%
0.7 13
 
< 0.1%
0.8 2055
 
1.4%
0.9 15
 
< 0.1%
ValueCountFrequency (%)
371 1
< 0.1%
367.6 1
< 0.1%
278.4 1
< 0.1%
268.6 1
< 0.1%
247.2 1
< 0.1%
240 1
< 0.1%
236.8 1
< 0.1%
225 1
< 0.1%
219.6 1
< 0.1%
216.3 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.0 KiB
False
110316 
True
31877 
ValueCountFrequency (%)
False 110316
77.6%
True 31877
 
22.4%
2025-07-05T11:43:21.213544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-07-05T11:43:11.860333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:41.500731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.814942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:47.380155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.404085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.227989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.792939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:54.395938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.252311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.859790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:59.865911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.411563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:03.983275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.542377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.981062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.876722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.389663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.955500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:41.783297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.903995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:47.504936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.489835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.318460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.895203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:54.487441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.338072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.948986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:00.009639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.497843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.067914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.626315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:07.071950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.960688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.473810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:12.100107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:41.962903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.999454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:47.656660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.579569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.407400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.989888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:54.826885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.430598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:58.069892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:00.157806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.589827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.156803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.706829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:07.157079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.046746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.570360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:12.241450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:42.136756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:44.110539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:47.782946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.666541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.503536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:53.082603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:54.932612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.521323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:58.167604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:00.294098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.684908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.247334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.797916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:07.656766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.135872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.659274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:12.382427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:42.279508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:44.204455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:47.905026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.773853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.601291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:53.168059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.016472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.648300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:58.256031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:00.422227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.777626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.346738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.887201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:07.746520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.230775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.742601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:12.521390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:42.461572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:44.297411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:48.060322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.871315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.689239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:53.267981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.128417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.749372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:58.360095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:00.940892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.875018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.446756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.971466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:07.833024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.321432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.830394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:12.671150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:42.643460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:44.419916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:48.211624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.964023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.796896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:53.361240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.217325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.845442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:58.466587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:01.072295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.974183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.543711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.063391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:07.920260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.413273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.922021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:12.815071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:42.807070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:44.665523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:48.366096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:50.059564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.890944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:53.450815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.310698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.932366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:58.559462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-05T11:42:48.514113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:50.158650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.991022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:53.544134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.396769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.032465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:58.650405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:01.367309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:03.160242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.719002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.235339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.082603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.599337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.093917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:13.073366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.069778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:45.124871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:48.659934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:50.249772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.081312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:53.633139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.490397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.124921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:58.792385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:01.497772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:03.252012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.810000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.315718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.173385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.686554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.178146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:13.227029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.161318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:45.266989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:48.756653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:50.339899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.171732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:53.725776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.577658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.211129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:58.923894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:01.649097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:03.358495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.899204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.398512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.264094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.768877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.261840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:13.380355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.252791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:45.422421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:48.848660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:50.435887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.266281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:53.829903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.673203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.301206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:59.081010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:01.800272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:03.450782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:04.988437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.500770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.353876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.856589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.349589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:13.520443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.346879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:45.579957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:48.941666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:50.758916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-05T11:42:53.934068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.760824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.390409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:59.212670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:01.942878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:03.535975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.078664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.583896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.447662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:09.940147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.432719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:13.650486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.452646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:46.308115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.033614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:50.845027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.440587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:54.015365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.845968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.469210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:59.331884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-05T11:43:03.618664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-05T11:43:06.658588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.544704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.021828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.509942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:13.801703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.551422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:46.726613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.127964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:50.936609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.525607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:54.117547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:55.959972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.555766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:59.466219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.130010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:03.707461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.259764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.735065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.624671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.121306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.605844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:13.934169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.637542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:47.122415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.216734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.042717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.608436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:54.206523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.058883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.640661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:59.604771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.215665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:03.794761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.347202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.815535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.704780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.217012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.694143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:14.064437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:43.722053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:47.243720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:49.308470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:51.129370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:52.691122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:54.295779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:56.144527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:57.748281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:42:59.729168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:02.316336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:03.882528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:05.450194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:06.891955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:08.782522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:10.299275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-05T11:43:11.773869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-05T11:43:21.286622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Cloud3pmCloud9amEvaporationHumidity3pmHumidity9amLocationMaxTempMinTempPressure3pmPressure9amRISK_MMRainTodayRainTomorrowRainfallSunshineTemp3pmTemp9amWindDir3pmWindDir9amWindGustDirWindGustSpeedWindSpeed3pmWindSpeed9am
Cloud3pm1.0000.616-0.2180.5400.3760.165-0.2890.010-0.080-0.1440.4590.2780.4030.322-0.719-0.332-0.1360.0460.0570.0570.0950.0170.046
Cloud9am0.6161.000-0.2100.5300.4820.172-0.2930.065-0.051-0.1230.3940.3170.3260.374-0.688-0.307-0.1480.0370.0580.0480.0580.0460.016
Evaporation-0.218-0.2101.000-0.409-0.5610.1170.6970.581-0.372-0.344-0.2010.0460.041-0.3100.4490.6770.6720.0160.0250.0190.2580.1810.210
Humidity3pm0.5400.530-0.4091.0000.6380.231-0.4610.0300.058-0.0220.5080.3850.4960.444-0.620-0.509-0.1830.0540.0860.046-0.0470.025-0.036
Humidity9am0.3760.482-0.5610.6381.0000.208-0.468-0.2220.1740.1270.3510.3740.2700.444-0.531-0.462-0.4450.0460.0730.048-0.233-0.158-0.288
Location0.1650.1720.1170.2310.2081.0000.3060.2830.1190.1080.0360.1570.1570.0360.1270.3050.2920.2000.2080.1980.1320.1620.165
MaxTemp-0.289-0.2930.697-0.461-0.4680.3061.0000.738-0.445-0.355-0.2220.2320.162-0.2990.4990.9850.8920.1040.1200.1060.0930.0650.024
MinTemp0.0100.0650.5810.030-0.2220.2830.7381.000-0.469-0.4650.0580.1000.0960.0220.1130.7100.8990.1060.0870.1030.2020.1820.180
Pressure3pm-0.080-0.051-0.3720.0580.1740.119-0.445-0.4691.0000.960-0.1970.1320.236-0.064-0.052-0.409-0.4810.0920.0780.080-0.382-0.238-0.162
Pressure9am-0.144-0.123-0.344-0.0220.1270.108-0.355-0.4650.9601.000-0.2330.2030.251-0.1550.014-0.311-0.4380.0860.0730.080-0.428-0.281-0.212
RISK_MM0.4590.394-0.2010.5080.3510.036-0.2220.058-0.197-0.2331.0000.1110.1890.412-0.524-0.258-0.0750.0110.0100.0130.2050.0740.070
RainToday0.2780.3170.0460.3850.3740.1570.2320.1000.1320.2030.1111.0000.3130.1890.3350.2400.1260.1420.1940.1470.1560.0800.093
RainTomorrow0.4030.3260.0410.4960.2700.1570.1620.0960.2360.2510.1890.3131.0000.1200.4520.2000.0540.0960.1290.1060.2390.1020.085
Rainfall0.3220.374-0.3100.4440.4440.036-0.2990.022-0.064-0.1550.4120.1890.1201.000-0.401-0.305-0.1550.0090.0120.0090.1260.0670.082
Sunshine-0.719-0.6880.449-0.620-0.5310.1270.4990.113-0.0520.014-0.5240.3350.452-0.4011.0000.5200.3310.0690.0880.080-0.0040.0590.029
Temp3pm-0.332-0.3070.677-0.509-0.4620.3050.9850.710-0.409-0.311-0.2580.2400.200-0.3050.5201.0000.8650.1060.1220.1080.0580.0450.013
Temp9am-0.136-0.1480.672-0.183-0.4450.2920.8920.899-0.481-0.438-0.0750.1260.054-0.1550.3310.8651.0000.1080.1020.1080.1750.1730.136
WindDir3pm0.0460.0370.0160.0540.0460.2000.1040.1060.0920.0860.0110.1420.0960.0090.0690.1060.1081.0000.2020.3470.0730.0490.053
WindDir9am0.0570.0580.0250.0860.0730.2080.1200.0870.0780.0730.0100.1940.1290.0120.0880.1220.1020.2021.0000.2430.0710.0580.047
WindGustDir0.0570.0480.0190.0460.0480.1980.1060.1030.0800.0800.0130.1470.1060.0090.0800.1080.1080.3470.2431.0000.0770.0480.055
WindGustSpeed0.0950.0580.258-0.047-0.2330.1320.0930.202-0.382-0.4280.2050.1560.2390.126-0.0040.0580.1750.0730.0710.0771.0000.6810.588
WindSpeed3pm0.0170.0460.1810.025-0.1580.1620.0650.182-0.238-0.2810.0740.0800.1020.0670.0590.0450.1730.0490.0580.0480.6811.0000.486
WindSpeed9am0.0460.0160.210-0.036-0.2880.1650.0240.180-0.162-0.2120.0700.0930.0850.0820.0290.0130.1360.0530.0470.0550.5880.4861.000

Missing values

2025-07-05T11:43:14.384670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-05T11:43:14.981195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-05T11:43:15.939997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRISK_MMRainTomorrow
02008-12-01Albury13.422.90.6NaNNaNW44.0WWNW20.024.071.022.01007.71007.18.0NaN16.921.8No0.0No
12008-12-02Albury7.425.10.0NaNNaNWNW44.0NNWWSW4.022.044.025.01010.61007.8NaNNaN17.224.3No0.0No
22008-12-03Albury12.925.70.0NaNNaNWSW46.0WWSW19.026.038.030.01007.61008.7NaN2.021.023.2No0.0No
32008-12-04Albury9.228.00.0NaNNaNNE24.0SEE11.09.045.016.01017.61012.8NaNNaN18.126.5No1.0No
42008-12-05Albury17.532.31.0NaNNaNW41.0ENENW7.020.082.033.01010.81006.07.08.017.829.7No0.2No
52008-12-06Albury14.629.70.2NaNNaNWNW56.0WW19.024.055.023.01009.21005.4NaNNaN20.628.9No0.0No
62008-12-07Albury14.325.00.0NaNNaNW50.0SWW20.024.049.019.01009.61008.21.0NaN18.124.6No0.0No
72008-12-08Albury7.726.70.0NaNNaNW35.0SSEW6.017.048.019.01013.41010.1NaNNaN16.325.5No0.0No
82008-12-09Albury9.731.90.0NaNNaNNNW80.0SENW7.028.042.09.01008.91003.6NaNNaN18.330.2No1.4Yes
92008-12-10Albury13.130.11.4NaNNaNW28.0SSSE15.011.058.027.01007.01005.7NaNNaN20.128.2Yes0.0No
DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRISK_MMRainTomorrow
1421832017-06-15Uluru2.622.50.0NaNNaNS19.0SE9.07.059.024.01025.01021.4NaNNaN8.822.1No0.0No
1421842017-06-16Uluru5.224.30.0NaNNaNE24.0SEE11.011.053.024.01023.81020.0NaNNaN12.323.3No0.0No
1421852017-06-17Uluru6.423.40.0NaNNaNESE31.0SESE15.017.053.025.01025.81023.0NaNNaN11.223.1No0.0No
1421862017-06-18Uluru8.020.70.0NaNNaNESE41.0SEE19.026.056.032.01028.11024.3NaN7.011.620.0No0.0No
1421872017-06-19Uluru7.420.60.0NaNNaNE35.0ESEE15.017.063.033.01027.21023.3NaNNaN11.020.3No0.0No
1421882017-06-20Uluru3.521.80.0NaNNaNE31.0ESEE15.013.059.027.01024.71021.2NaNNaN9.420.9No0.0No
1421892017-06-21Uluru2.823.40.0NaNNaNE31.0SEENE13.011.051.024.01024.61020.3NaNNaN10.122.4No0.0No
1421902017-06-22Uluru3.625.30.0NaNNaNNNW22.0SEN13.09.056.021.01023.51019.1NaNNaN10.924.5No0.0No
1421912017-06-23Uluru5.426.90.0NaNNaNN37.0SEWNW9.09.053.024.01021.01016.8NaNNaN12.526.1No0.0No
1421922017-06-24Uluru7.827.00.0NaNNaNSE28.0SSEN13.07.051.024.01019.41016.53.02.015.126.0No0.0No